Predictive Modeling of Anatomy with Genetic and Clinical Data
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Predictive modeling.pdf
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Author(s)
Dalca, Adrian Vasile
Sridharan, Ramesh
Sabuncu, Mert R
Golland, Polina
Date Issued
November 2015
Journal
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
Publisher
Springer
Citation
Dalca, Adrian V., et al. “Predictive Modeling of Anatomy with Genetic and Clinical Data.” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by Nassir Navab et al., vol. 9351, Springer International Publishing, 2015, pp. 519–26.
Version
Author's final manuscript
Abstract
We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject’s health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient’s scans to the predicted subject-specific healthy anatomical trajectory. Keywords: Population Trend, Baseline Image, Kernel Machine, Good Linear Unbiased Predictor, Segmentation Label
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-24574-4_62
